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Record W3198114764 · doi:10.17719/jisr.2021.35884

IN THE CONTEXT OF MAYYAFARIQINS ISLAMIZATION PROCESS AND MUSLIM-NON-MUSLIM RELATIONS: SILVAN CHALDEAN CHURCH CONSTRUCTION EXAMPLE

2021· article· en· W3198114764 on OpenAlexaboutno aff
mer Akta

Bibliographic record

VenueJournal of International Social Research · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical and Linguistic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIslamizationIslamWorshipPersecutionHistoryReligious persecutionContext (archaeology)ReignQuarter (Canadian coin)PoliticsHuman settlementSociologyReligious studiesAncient historyPolitical scienceLawPhilosophyArchaeology

Abstract

fetched live from OpenAlex

The beginning of Islamic conquests, next to new settlements, allowed people of different cultures and religions to enter the rule of Muslims. One of the best examples of this is the Silvan experience, whose former name was Meyyâfârikin. Meyyâfârikin, conquered during the reign of Hz. Omar, maintained its social, cultural, ethnic and religious wealth until the last years of the Ottoman Empire. So much so that non-Muslims living here have benefited from the tolerance of Muslims and lived their religion freely in places of worship. In addition to the persecution and violence seen in many societies, except Islamic societies, during the historical process, religious and political pressures were not applied to non-Muslims. Here in this article topics will be discussed about the construction process of Silvan Chaldean Church, which is one of the best examples of Islam and Muslims' tolerance towards non-Muslims and was built in the last quarter of the 19th century. In the study, firstly, historical information about Meyyafarikin will be given, and then the information about the construction of the church will be tried to be explained by making use of archive documents. The information obtained will be handled and evaluated with a chronological approach.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.253
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.089
GPT teacher head0.435
Teacher spread0.346 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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